Research Workbench
Structured environments for experiments, validation reviews, and consistent strategy comparisons.
Platform
Tools
Quant tooling designed for repeatable research and operational clarity.
Code & Kapital offers software around the real work of quantitative research: backtesting software, strategy validation, factor diagnostics, portfolio analytics, and data workflow visibility.
Tooling principles
The platform is organized around disciplined workflow design, not novelty. The focus is on removing ambiguity from the research process and making assumptions observable.
For Research teams and independent quants
Backtesting Engine
A best-in-class, Python-native backtesting framework built for institutional-grade strategy evaluation—featuring bias-aware simulation and production-ready assumptions.
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For PMs, analysts, and allocators
Portfolio Diagnostics
End-to-end portfolio analytics for performance, risk, and implementation—tracking returns, Sharpe ratios, VaR, drawdowns, turnover, factor exposures, and beyond.
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For Systematic strategy developers
Signal
Explorer
Institutional-grade signal research reports designed to evaluate quantitative signals—delivering returns, performance analytics, signal diagnostics, and cross-signal comparisons.
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For Research teams and independent quants
Regime
Lab
Institutional-quality regime analytics for identifying and tracking market states—analyzing macro conditions, cross-asset behavior, and regime transitions.
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For Research teams and PMs
Factor
Lab
Advanced signal diagnostics for evaluating quality and durability—spanning information decay, factor exposures, crowding context, and cross-sectional robustness.
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Platform Layers
How the platform fits together.
Research, diagnostics, and operational visibility reinforce one another inside a system designed for serious practitioners.
Diagnostics Layer
Factor and portfolio inspection tools that expose implementation trade-offs earlier in the process.
Operational Visibility
Pipeline monitoring and workflow status surfaces that reduce silent failure across daily research operations.